Chapter 7

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Chapter 7 End-to-End Data

• Two main topics
• Presentation formatting
• Compression
• We will go over the main issues in presentation
  formatting, but not much detail
• More detail will be covered in compression,
  especially JPEG and MPEG

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Presentation Formatting/Encoding

• The receiver must be able to extract the same
  message from the signal as the transmitter sent
• Encoding is sometimes called argument marshalling
• Marshalling is actually not trivial because
  compilers and application programs have a lot of
  latitude in how they lay out structures (records)
• Look over the next high-level graphic

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Application
Application
data
data
Presentation
Presentation
encoding
decoding

Message
Message
Message
4
Taxonomy

• Base types lowest level
• Integers, floating point, characters, etc.
• Flat types
• Structures
• Arrays
• Complex types highest level
• Types requiring pointers

5
Examples

• Case 1 sending an ordered string of integers
  (say financial market data) over the internet
  no problem breaking this up into a string of
  bytes and no problem reassembling the data at the
  end

6
Examples continued

• Case 2 sending a database of student records
  over a network.
• Students would have different numbers of courses
  that took, so the records would be of different
  length but the fields would probably be fixed
  length
• Packing and unpacking the data would have some
  difficulties involved

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Examples continued

• Case 3 a hierarchical database stored in a
  format with pointers needs to be transmitted over
  the Internet
• Packing and unpacking is a large problem
• Pointers are implemented by memory addresses and
  will change from one machine (sender) to another
  (receiver)
• Marshalling must serialize a complex, pointer
  implementation of a database quite difficult!

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Two Conversion Strategies

• Sender converts to common format, common format
  is transmitted, receiver decodes from common
  format
• Seems natural, but
• Receiver-makes-right transmit and let the
  receiver figure it all out
• Surprisingly this is often the better approach
• See the reasons on page 533

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Interface
descriptor for
Procedure P
Call P
P
Arguments
Specification
Arguments
Code
Code
Client
Stub
Server
stub
compiler
stub
Marshalled
Marshalled
arguments
arguments
RPC
RPC
Message
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Data Compression

• Blue.bmp 293 KB
• Blue.jpeg 4 KB
• Not much information
• Length, width of each area
• Color of each area

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Data Compression - Why

• Bandwidth is a scarce resource, someone still has
  to pay for it
• Often important to compress the data at the
  sender then transmit the compressed form then
  decompress it at the receiver
• .bmp is a good format for application programs
  like Paint but it is much better to transmit
  with the .jpeg file format

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Two classes of Compression

• Lossless
• Data recovered from the compression/decompression
  process is the same as the original
• Lossy
• Some information might be removed by the
  compression/decompression process

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Why not always Lossless?

• Lossy algorithms typically achieve an order of
  magnitude (10x) better compression than a
  lossless algorithm
• 10x makes a big difference in the amount of data
  that must be transmitted
• Still images, video and audio are all intended
  for human eyes or ears which can tolerate
  errors and imperfections because the brain can
  compensate

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Lossless Algorithms

• Run length encoding (RLE)
• Replaces consecutive occurrences of a symbol with
  a single symbol and the number of times it occurs
  (example AAACCCC is 3A4C)
• Differential Pulse Code Modulation
• Records differences from the base symbol
• Dictionary-Based
• Each string is replace with its index in a
  dictionary

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Lossless Example Differential Encoding

• Basic idea is to encode changes. Concept is also
  used in some lossy algorithms
• No need to store all the information in each of
  the following pictures for the last two just
  the changes which are much smaller
• Frame 1 A B C Frame 2 A B C D E F then just
  store D E F for Frame 2 and add it to Frame 1
  to restore Frame 2

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Image Compression (JPEG)

• JPEG Joint Photographic Experts Group
• More than a compression algorithm, also defines
  the format for image or video data
• JPEG compression takes place in stages
• Aside first Fourier transforms and filtering

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Fourier Transform

• Consider the following graph
• It is a weighted sum of 5 sine waves
• But the coefficients of the higher frequency
  terms are very small
• So the entire figure can be approximated well by
  the low order terms

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1st Order Approximation

• Only the first term not a good approximation

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2nd Order Approximation

• The first two terms give a better approximation

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4th Order Approximation

• Skipping ahead to 4 terms the approximation is
  excellent almost exact

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5th Order Approximation would be Exact

• Since the original function is a weighted sum of
  the first 5 sin terms - sin(kt) - the information
  that uniquely represents the function is the set
  of coefficients 10521.5
• As we saw we could drop the .5 coefficient and
  retain most of the shape of the curve hence our
  information loss would be very slight. A simple
  example of lossy.

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Fourier vs. DCT

• The Discrete Cosine Transform (DCT) is very
  similar to the Fourier Transform (see pages 544-5
  and note that we are using a 2-dimensional
  transform)

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JPEG compression
Source
Compressed
image
image
DCT
Quantization
Encoding
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MPEG

• A very difficult algorithm!
• Like JPEG for a single frame, but it has three
  basic kinds of frames
• Encoding is very difficult and computationally
  intensive, hence slow, often done offline
• Decoding is the only part usually done real time

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Three Phases

• Study over the three phases of JPEG
• DCT
• Quantization
• similar to our example of dropping the 5th
  coefficient and retaining a graph that was very
  similar to the original
• Encoding phase

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Input
Frame 1
Frame 2
Frame 3
Frame 4
Frame 5
Frame 6
Frame 7
stream
MPEG
compression
Forward
prediction
Compressed
I frame
B frame
B frame
P frame
B frame
B frame
I frame
stream
Bidirectional
prediction
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Color frame

16
16 macroblock
with Y component

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16
pixel region

8
8 macroblock
with U component

8
8 macroblock
with V component
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SeqHdr
Group of pictures
SeqHdr
Group of pictures
SeqEndCode

GOPHdr
Picture
Picture
Picture

Slice
PictureHdr
Slice
Slice

Macroblock
Macroblock
SliceHdr
Macroblock
MBHdr
Block(0)
Block(1)
Block(2)
Block(3)
Block(4)
Block(5)